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Set up Travis #2

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merged 4 commits into from
Sep 12, 2019
Merged

Set up Travis #2

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d7eca75
add .travis.yml
xukai92 Sep 12, 2019
910c8a2
add budgets
xukai92 Sep 12, 2019
5ef87d2
fix extra deps
xukai92 Sep 12, 2019
d7a3910
update constraint name
xukai92 Sep 12, 2019
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22 changes: 22 additions & 0 deletions .travis.yml
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@@ -0,0 +1,22 @@
language: julia

coveralls: true

os:
- linux

julia:
- 1.1
- 1.2
- nightly

matrix:
allow_failures:
- julia: nightly
fast_finish: true

after_success:
- julia -e 'Pkg.add("Coverage"); using Coverage; Codecov.submit(process_folder())'

notifications:
email: false
2 changes: 1 addition & 1 deletion Project.toml
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Expand Up @@ -10,7 +10,7 @@ LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
Parameters = "d96e819e-fc66-5662-9728-84c9c7592b0a"
Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"

[extra]
[extras]
Distances = "b4f34e82-e78d-54a5-968a-f98e89d6e8f7"
Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"

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9 changes: 7 additions & 2 deletions README.md
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Expand Up @@ -2,11 +2,16 @@

A Julia package for density ratio estimation

Supported methods
- Infinite moment matching based on maximum mean discrepancy (MMD)
[![Build Status](https://travis-ci.com/xukai92/DensityRatioEstimation.jl.svg?branch=master)](https://travis-ci.com/xukai92/DensityRatioEstimation.jl) [![Coverage Status](https://coveralls.io/repos/github/xukai92/DensityRatioEstimation.jl/badge.svg)](https://coveralls.io/github/xukai92/DensityRatioEstimation.jl)

To get started, see the [introduction](https://htmlpreview.github.io/?https://github.com/xukai92/DensityRatioEstimation.jl/blob/master/docs/intro.html).

## Supported methods

Infinite moment matching based on maximum mean discrepancy (MMD)
- Numerical solution with optional positivity and normalisation constraints
- Analytical solution with no constraint

## References

Sugiyama M, Suzuki T, Kanamori T. Density ratio estimation in machine learning. Cambridge University Press, 2012.
4 changes: 2 additions & 2 deletions src/moment_matching.jl
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Expand Up @@ -48,7 +48,7 @@ function estimate_ratio(mmd::AbstractMMD, x_de, x_nu; σs=nothing)
end

@with_kw struct MMDNumerical <: AbstractMMD
positive::Bool=true
positivity::Bool=true
normalisation::Bool=true
end

Expand All @@ -57,7 +57,7 @@ function _estimate_ratio(mmd::MMDNumerical, Kdede, Kdenu)
model = Model(with_optimizer(IpoptOptimizer; print_level=0))
@variable(model, r[1:n_de])
@objective(model, Min, 1 / n_de ^ 2 * sum(r[i] * Kdede[i,j] * r[j] for i = 1:n_de, j=1:n_de) - 2 / (n_de * n_nu) * sum(r[i] * Kdenu[i,j] for i = 1:n_de, j=1:n_nu))
if mmd.positive
if mmd.positivity
@constraint(model, r .>= 0)
end
if mmd.normalisation
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